AI-Powered Anomaly Detection with Blockchain for Real-Time Security and Reliability in Autonomous Vehicles (2505.06632v1)

Abstract: Autonomous Vehicles (AV) proliferation brings important and pressing security and reliability issues that must be dealt with to guarantee public safety and help their widespread adoption. The contribution of the proposed research is towards achieving more secure, reliable, and trustworthy autonomous transportation system by providing more capabilities for anomaly detection, data provenance, and real-time response in safety critical AV deployments. In this research, we develop a new framework that combines the power of AI for real-time anomaly detection with blockchain technology to detect and prevent any malicious activity including sensor failures in AVs. Through Long Short-Term Memory (LSTM) networks, our approach continually monitors associated multi-sensor data streams to detect anomalous patterns that may represent cyberattacks as well as hardware malfunctions. Further, this framework employs a decentralized platform for securely storing sensor data and anomaly alerts in a blockchain ledger for data incorruptibility and authenticity, while offering transparent forensic features. Moreover, immediate automated response mechanisms are deployed using smart contracts when anomalies are found. This makes the AV system more resilient to attacks from both cyberspace and hardware component failure. Besides, we identify potential challenges of scalability in handling high frequency sensor data, computational constraint in resource constrained environment, and of distributed data storage in terms of privacy.

• The paper presents a framework combining AI-powered anomaly detection and blockchain technology to enhance the real-time security and reliability of autonomous vehicles.
• It employs LSTM networks to monitor sensor data for anomalies and a blockchain ledger with smart contracts for data integrity and automated response.
• Experimental validation in the CARLA simulator shows 94.7% precision in anomaly detection and highlights potential while noting challenges like scalability and computational overhead.

AI-Powered Anomaly Detection with Blockchain for Real-Time Security and Reliability in Autonomous Vehicles

The paper presents a comprehensive framework designed to enhance the security and reliability of autonomous vehicles (AVs) through the integration of AI for anomaly detection and blockchain technology for data integrity. The primary motivation behind this research is the critical need to address the security vulnerabilities and reliability issues inherent in AV systems, which are increasingly becoming pivotal in modern transportation infrastructures. This work seeks to create a robust AV architecture capable of identifying and mitigating malicious activities and sensor failures in real time.

The proposed framework employs Long Short-Term Memory (LSTM) networks to monitor the data streams from multiple sensors, such as LiDAR, radar, cameras, and GPS. The AI component is responsible for detecting anomalous patterns that may signify cyberattacks or hardware malfunctions. The paper highlights the superior capability of LSTM networks over classical machine learning methods in capturing time dependencies in sequential data, which is crucial for the real-time detection of anomalies in AV operations.

Complementing the AI-driven anomaly detection is a blockchain-based system that provides a decentralized and tamper-resistant ledger. This blockchain layer records critical sensor data and anomaly detection results, ensuring data incorruptibility, authenticity, and transparency. Additionally, the framework incorporates smart contracts to automate immediate response mechanisms when anomalies are detected, thereby enhancing the resilience of AV systems against both cyber and hardware failures.

The experimental validation of the proposed system was conducted using the CARLA autonomous driving simulator, where various attack scenarios such as GPS spoofing and LiDAR manipulation were simulated. The results demonstrated the framework's efficacy, achieving a 94.7% precision in anomaly detection and highlighting its capability to rapidly respond to security threats. The blockchain component was tested for performance under different network loads, exhibiting promising transaction processing rates and confirmation times suitable for real-time applications.

A significant strength of this framework is its ability to combine real-time AI anomaly detection with blockchain's immutable ledger, creating multiple layers of defense and enhancing the trust required for AV adoption. However, the paper acknowledges challenges related to scalability and computational overhead, particularly regarding blockchain's ability to handle high-frequency, high-volume sensor data. Privacy concerns due to the permanent storage of vehicle operational data are also noted as an area needing careful consideration.

In conclusion, this paper presents a forward-looking strategy for improving AV security through the integration of AI and blockchain technologies. While the framework demonstrates substantial potential in enhancing real-time reaction to security threats and ensuring data integrity, future work is necessary to address scalability issues and optimize the system for practical deployment in AV networks. This research contributes a significant step towards achieving secure, reliable, and trustworthy autonomous transportation systems.